219 citations · 904 across the 50 of their papers we have counts for
36 papers · 1 filter
Adaptive Fuzzy C-Means with Graph Embedding
Qiang Chen, Weizhong Yu, Feiping Nie +1
Fuzzy clustering algorithms can be roughly categorized into two main groups: Fuzzy C-Means (FCM) based methods and mixture model based methods. However, for almost all existing FCM…
Robust Capped lp-Norm Support Vector Ordinal Regression
Haorui Xiang, Zhichang Wu, Guoxu Li +3
Ordinal regression is a specialized supervised problem where the labels show an inherent order. The order distinguishes it from normal multi-class problem. Support Vector Ordinal R…
Multi-class Support Vector Machine with Maximizing Minimum Margin
Zhezheng Hao, Feiping Nie, Rong Wang
Support Vector Machine (SVM) stands out as a prominent machine learning technique widely applied in practical pattern recognition tasks. It achieves binary classification by maximi…
A Novel Normalized-Cut Solver with Nearest Neighbor Hierarchical Initialization
Feiping Nie, Jitao Lu, Danyang Wu +2
Normalized-Cut (N-Cut) is a famous model of spectral clustering. The traditional N-Cut solvers are two-stage: 1) calculating the continuous spectral embedding of normalized Laplaci…
AGFormer: Efficient Graph Representation with Anchor-Graph Transformer
Bo Jiang, Fei Xu, Ziyan Zhang +2
To alleviate the local receptive issue of GCN, Transformers have been exploited to capture the long range dependences of nodes for graph data representation and learning. However,…
Joint Feature and Differentiable -NN Graph Learning using Dirichlet Energy
Lei Xu, Lei Chen, Rong Wang +2
Feature selection (FS) plays an important role in machine learning, which extracts important features and accelerates the learning process. In this paper, we propose a deep FS meth…
On the Global Solution of Soft k-Means
Feiping Nie, Hong Chen, Rong Wang +1
This paper presents an algorithm to solve the Soft k-Means problem globally. Unlike Fuzzy c-Means, Soft k-Means (SkM) has a matrix factorization-type objective and has been shown t…
Adaptive neighborhood Metric learning
Kun Song, Junwei Han, Gong Cheng +2
In this paper, we reveal that metric learning would suffer from serious inseparable problem if without informative sample mining. Since the inseparable samples are often mixed with…
Compactness Score: A Fast Filter Method for Unsupervised Feature Selection
Peican Zhu, Xin Hou, Keke Tang +2
Along with the flourish of the information age, massive amounts of data are generated day by day. Due to the large-scale and high-dimensional characteristics of these data, it is o…
New Tight Relaxations of Rank Minimization for Multi-Task Learning
Wei Chang, Feiping Nie, Rong Wang +1
Multi-task learning has been observed by many researchers, which supposes that different tasks can share a low-rank common yet latent subspace. It means learning multiple tasks joi…
Sparse PCA via -Norm Regularization for Unsupervised Feature Selection
Zhengxin Li, Feiping Nie, Jintang Bian +1
In the field of data mining, how to deal with high-dimensional data is an inevitable problem. Unsupervised feature selection has attracted more and more attention because it does n…
Self-Weighted Robust LDA for Multiclass Classification with Edge Classes
Caixia Yan, Xiaojun Chang, Minnan Luo +4
Linear discriminant analysis (LDA) is a popular technique to learn the most discriminative features for multi-class classification. A vast majority of existing LDA algorithms are p…
NP-PROV: Neural Processes with Position-Relevant-Only Variances
Xuesong Wang, Lina Yao, Xianzhi Wang +1
Neural Processes (NPs) families encode distributions over functions to a latent representation, given context data, and decode posterior mean and variance at unknown locations. Sin…
Agglomerative Neural Networks for Multi-view Clustering
Zhe Liu, Yun Li, Lina Yao +2
Conventional multi-view clustering methods seek for a view consensus through minimizing the pairwise discrepancy between the consensus and subviews. However, the pairwise compariso…
Supervised feature selection with orthogonal regression and feature weighting
Xia Wu, Xueyuan Xu, Jianhong Liu +3
Effective features can improve the performance of a model, which can thus help us understand the characteristics and underlying structure of complex data. Previous feature selectio…
Robust and Efficient Fuzzy C-Means Clustering Constrained on Flexible Sparsity
Jinglin Xu, Junwei Han, Mingliang Xu +2
Clustering is an effective technique in data mining to group a set of objects in terms of some attributes. Among various clustering approaches, the family of K-Means algorithms gai…
An Iteratively Re-weighted Method for Problems with Sparsity-Inducing Norms
Feiping Nie, Zhanxuan Hu, Xiaoqian Wang +3
This work aims at solving the problems with intractable sparsity-inducing norms that are often encountered in various machine learning tasks, such as multi-task learning, subspace…
Robust Linear Discriminant Analysis Using Ratio Minimization of L1,2-Norms
Feiping Nie, Hua Wang, Zheng Wang +1
As one of the most popular linear subspace learning methods, the Linear Discriminant Analysis (LDA) method has been widely studied in machine learning community and applied to many…
Intrinsic Weight Learning Approach for Multi-view Clustering
Feiping Nie, Jing Li, Xuelong Li
Exploiting different representations, or views, of the same object for better clustering has become very popular these days, which is conventionally called multi-view clustering. G…
Learning Feature Sparse Principal Components
Lai Tian, Feiping Nie, Xuelong Li
This paper presents new algorithms to solve the feature-sparsity constrained PCA problem (FSPCA), which performs feature selection and PCA simultaneously. Existing optimization met…
Feature Learning Viewpoint of AdaBoost and a New Algorithm
Fei Wang, Zhongheng Li, Fang He +3
The AdaBoost algorithm has the superiority of resisting overfitting. Understanding the mysteries of this phenomena is a very fascinating fundamental theoretical problem. Many studi…
Regularized Singular Value Decomposition and Application to Recommender System
Shuai Zheng, Chris Ding, Feiping Nie
Singular value decomposition (SVD) is the mathematical basis of principal component analysis (PCA). Together, SVD and PCA are one of the most widely used mathematical formalism/dec…
Ranking with Adaptive Neighbors
Muge Li, Liangyue Li, Feiping Nie
Retrieving the most similar objects in a large-scale database for a given query is a fundamental building block in many application domains, ranging from web searches, visual, cros…
Local Shrunk Discriminant Analysis (LSDA)
Zan Gao, Guotai Zhang, Feiping Nie +1
Dimensionality reduction is a crucial step for pattern recognition and data mining tasks to overcome the curse of dimensionality. Principal component analysis (PCA) is a traditiona…